@InProceedings{wen-EtAl:2017:EACLlong,
  author    = {Wen, Tsung-Hsien  and  Vandyke, David  and  Mrk\v{s}i\'{c}, Nikola  and  Gasic, Milica  and  Rojas Barahona, Lina M.  and  Su, Pei-Hao  and  Ultes, Stefan  and  Young, Steve},
  title     = {A Network-based End-to-End Trainable Task-oriented Dialogue System},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {438--449},
  abstract  = {Teaching machines to accomplish tasks by conversing naturally with humans is
	challenging. Currently, developing task-oriented dialogue systems requires
	creating multiple components and typically this involves either a large amount
	of handcrafting, or acquiring costly labelled datasets to solve a statistical
	learning problem for each component. In this work we introduce a neural
	network-based text-in, text-out end-to-end trainable goal-oriented dialogue
	system along with a new way of collecting dialogue data based on a novel
	pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue
	systems easily and without making too many assumptions about the task at hand.
	The results show that the model can converse with human subjects naturally
	whilst helping them to accomplish tasks in a restaurant search domain.},
  url       = {http://www.aclweb.org/anthology/E17-1042}
}